Overview

Dataset statistics

Number of variables16
Number of observations193272
Missing cells113396
Missing cells (%)3.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.0 MiB
Average record size in memory114.0 B

Variable types

Numeric6
Categorical8
Boolean2

Alerts

is_retweet has constant value "False" Constant
user_name has a high cardinality: 75869 distinct values High cardinality
user_location has a high cardinality: 23054 distinct values High cardinality
user_description has a high cardinality: 73957 distinct values High cardinality
user_created has a high cardinality: 77112 distinct values High cardinality
date has a high cardinality: 187514 distinct values High cardinality
text has a high cardinality: 191553 distinct values High cardinality
hashtags has a high cardinality: 52470 distinct values High cardinality
source has a high cardinality: 334 distinct values High cardinality
user_friends is highly correlated with user_favouritesHigh correlation
user_favourites is highly correlated with user_friendsHigh correlation
retweets is highly correlated with favoritesHigh correlation
favorites is highly correlated with retweetsHigh correlation
retweets is highly correlated with favoritesHigh correlation
favorites is highly correlated with retweetsHigh correlation
user_friends is highly correlated with user_favouritesHigh correlation
user_favourites is highly correlated with user_friendsHigh correlation
retweets is highly correlated with favoritesHigh correlation
favorites is highly correlated with retweetsHigh correlation
user_verified is highly correlated with is_retweetHigh correlation
is_retweet is highly correlated with user_verifiedHigh correlation
retweets is highly correlated with favoritesHigh correlation
favorites is highly correlated with retweetsHigh correlation
user_location has 58573 (30.3%) missing values Missing
user_description has 13730 (7.1%) missing values Missing
hashtags has 40973 (21.2%) missing values Missing
user_friends is highly skewed (γ1 = 41.73075676) Skewed
retweets is highly skewed (γ1 = 133.5361123) Skewed
favorites is highly skewed (γ1 = 77.54535765) Skewed
date is uniformly distributed Uniform
text is uniformly distributed Uniform
id has unique values Unique
user_friends has 23193 (12.0%) zeros Zeros
user_favourites has 8692 (4.5%) zeros Zeros
retweets has 144849 (74.9%) zeros Zeros
favorites has 101340 (52.4%) zeros Zeros

Reproduction

Analysis started2021-10-30 23:04:41.907294
Analysis finished2021-10-30 23:05:33.911031
Duration52 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

UNIQUE

Distinct193272
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.399486344 × 1018
Minimum1.337727768 × 1018
Maximum1.440388348 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-10-30T17:05:34.034701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.337727768 × 1018
5-th percentile1.362014467 × 1018
Q11.379804354 × 1018
median1.402808652 × 1018
Q31.418841921 × 1018
95-th percentile1.434428058 × 1018
Maximum1.440388348 × 1018
Range1.026605806 × 1017
Interquartile range (IQR)3.903756724 × 1016

Descriptive statistics

Standard deviation2.382255178 × 1016
Coefficient of variation (CV)0.01702235386
Kurtosis-0.9124554159
Mean1.399486344 × 1018
Median Absolute Deviation (MAD)2.026173309 × 1016
Skewness-0.2549031865
Sum-3.083629839 × 1018
Variance5.675139732 × 1032
MonotonicityNot monotonic
2021-10-30T17:05:34.228183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.377661711 × 10181
 
< 0.1%
1.37755881 × 10181
 
< 0.1%
1.430252041 × 10181
 
< 0.1%
1.42017988 × 10181
 
< 0.1%
1.379588009 × 10181
 
< 0.1%
1.429402915 × 10181
 
< 0.1%
1.382713297 × 10181
 
< 0.1%
1.359931436 × 10181
 
< 0.1%
1.372263586 × 10181
 
< 0.1%
1.403564962 × 10181
 
< 0.1%
Other values (193262)193262
> 99.9%
ValueCountFrequency (%)
1.337727768 × 10181
< 0.1%
1.337728702 × 10181
< 0.1%
1.337732077 × 10181
< 0.1%
1.337732996 × 10181
< 0.1%
1.337733049 × 10181
< 0.1%
1.337733857 × 10181
< 0.1%
1.337733928 × 10181
< 0.1%
1.33773407 × 10181
< 0.1%
1.337735596 × 10181
< 0.1%
1.337739608 × 10181
< 0.1%
ValueCountFrequency (%)
1.440388348 × 10181
< 0.1%
1.440387788 × 10181
< 0.1%
1.440387179 × 10181
< 0.1%
1.440385611 × 10181
< 0.1%
1.440385512 × 10181
< 0.1%
1.440385429 × 10181
< 0.1%
1.440384772 × 10181
< 0.1%
1.440384657 × 10181
< 0.1%
1.440383372 × 10181
< 0.1%
1.440383191 × 10181
< 0.1%

user_name
Categorical

HIGH CARDINALITY

Distinct75869
Distinct (%)39.3%
Missing1
Missing (%)< 0.1%
Memory size1.5 MiB
CowinBangalore
 
11495
CoWIN Blore 18-44
 
8879
Owl 🦉
 
6570
VaxBLR
 
3804
Workout Solutions
 
2251
Other values (75864)
160272 

Length

Max length50
Median length13
Mean length13.74027661
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57481 ?
Unique (%)29.7%

Sample

1st rowRachel Roh
2nd rowAlbert Fong
3rd roweli🇱🇹🇪🇺👌
4th rowCharles Adler
5th rowCitizen News Channel

Common Values

ValueCountFrequency (%)
CowinBangalore11495
 
5.9%
CoWIN Blore 18-448879
 
4.6%
Owl 🦉6570
 
3.4%
VaxBLR3804
 
2.0%
Workout Solutions2251
 
1.2%
Owl🦉1956
 
1.0%
eLéPhunk688
 
0.4%
Xukki🌍614
 
0.3%
Sputnik V443
 
0.2%
China Economy402
 
0.2%
Other values (75859)156169
80.8%

Length

2021-10-30T17:05:34.442612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cowinbangalore11495
 
2.8%
blore8884
 
2.2%
cowin8881
 
2.2%
18-448879
 
2.2%
owl6579
 
1.6%
🦉6575
 
1.6%
the4555
 
1.1%
news4082
 
1.0%
3952
 
1.0%
vaxblr3804
 
0.9%
Other values (71995)339537
83.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

user_location
Categorical

HIGH CARDINALITY
MISSING

Distinct23054
Distinct (%)17.1%
Missing58573
Missing (%)30.3%
Memory size1.5 MiB
Bengaluru, India
13397 
India
 
6690
Toronto, Canada and Worldwide
 
2619
New Delhi, India
 
2598
Mumbai, India
 
1670
Other values (23049)
107725 

Length

Max length135
Median length14
Mean length14.30746331
Min length1

Characters and Unicode

Total characters22
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15155 ?
Unique (%)11.3%

Sample

1st rowLa Crescenta-Montrose, CA
2nd rowSan Francisco, CA
3rd rowYour Bed
4th rowVancouver, BC - Canada
5th rowBirmingham, England

Common Values

ValueCountFrequency (%)
Bengaluru, India13397
 
6.9%
India6690
 
3.5%
Toronto, Canada and Worldwide2619
 
1.4%
New Delhi, India2598
 
1.3%
Mumbai, India1670
 
0.9%
United States1652
 
0.9%
New Delhi1037
 
0.5%
Sri Lanka1036
 
0.5%
Beijing, China919
 
0.5%
Mumbai859
 
0.4%
Other values (23044)102222
52.9%
(Missing)58573
30.3%

Length

2021-10-30T17:05:34.648061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
india33916
 
11.8%
bengaluru13905
 
4.8%
new6860
 
2.4%
delhi5762
 
2.0%
canada5146
 
1.8%
usa5089
 
1.8%
toronto4250
 
1.5%
united3930
 
1.4%
and3484
 
1.2%
3383
 
1.2%
Other values (16753)201230
70.1%

Most occurring characters

ValueCountFrequency (%)
22
100.0%

Most occurring categories

ValueCountFrequency (%)
Control22
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common22
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
22
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII22
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22
100.0%

user_description
Categorical

HIGH CARDINALITY
MISSING

Distinct73957
Distinct (%)41.2%
Missing13730
Missing (%)7.1%
Memory size1.5 MiB
Follow us to get update as soon as 18 plus vaccine appointments open up in Bangalore & Covaxin for 45+ #TwitterBot Telegram: https://t.co/WavSl590z5
 
11418
Get instant alerts for Free/Paid slots in BBMP, Bangalore Urban centers https://t.co/Vh4GH7Bb21
 
8854
follow for updates on 18-44 vaccination availability in Mumbai/Thane | #MumbaiCOVAXIN #ThaneCOVAXIN #MumbaiCOVISHIELD
 
4827
Hourly updates on FREE and PAID 18+ and 45+ vaccine slot availability across #Bengaluru BBMP,URBAN & RURAL
 
3803
George Tsanis – Workout Solutions Health and Fitness Consultants since 1996 – One-on-one and online distance coaching – Toronto, Canada, World
 
2619
Other values (73952)
148021 

Length

Max length248
Median length116
Mean length106.6371211
Min length1

Characters and Unicode

Total characters104470
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57512 ?
Unique (%)32.0%

Sample

1st rowAggregator of Asian American news; scanning diverse sources 24/7/365. RT's, Follows and 'Likes' will fuel me 👩‍💻
2nd rowMarketing dude, tech geek, heavy metal & '80s music junkie. Fascinated by meteorology and all things in the cloud. Opinions are my own.
3rd rowheil, hydra 🖐☺
4th rowHosting "CharlesAdlerTonight" Global News Radio Network. Weeknights 7 Pacific-10 Eastern - Email comments/ideas to charles@charlesadlertonight.ca
5th rowCitizen News Channel bringing you an alternative news source from citizen journalists that haven't sold out. Real news & real views

Common Values

ValueCountFrequency (%)
Follow us to get update as soon as 18 plus vaccine appointments open up in Bangalore & Covaxin for 45+ #TwitterBot Telegram: https://t.co/WavSl590z511418
 
5.9%
Get instant alerts for Free/Paid slots in BBMP, Bangalore Urban centers https://t.co/Vh4GH7Bb218854
 
4.6%
follow for updates on 18-44 vaccination availability in Mumbai/Thane | #MumbaiCOVAXIN #ThaneCOVAXIN #MumbaiCOVISHIELD4827
 
2.5%
Hourly updates on FREE and PAID 18+ and 45+ vaccine slot availability across #Bengaluru BBMP,URBAN & RURAL3803
 
2.0%
George Tsanis – Workout Solutions Health and Fitness Consultants since 1996 – One-on-one and online distance coaching – Toronto, Canada, World2619
 
1.4%
follow for updates on 18-44 vaccination availability in Gurgaon | Search: #GurgaonCOVISHIELD #GurgaonCOVAXIN1831
 
0.9%
follow for updates on 18-44 vaccination availability in Pune1697
 
0.9%
Latest business news and valuable information from China.402
 
0.2%
Sputnik is a global wire, radio and digital news service. We exist to tell the stories that aren’t being told.384
 
0.2%
News https://t.co/655UNA7k59 https://t.co/rpTzREEniO368
 
0.2%
Other values (73947)143339
74.2%
(Missing)13730
 
7.1%

Length

2021-10-30T17:05:34.876450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
120590
 
4.4%
and60102
 
2.2%
in54877
 
2.0%
for49618
 
1.8%
the46342
 
1.7%
of38557
 
1.4%
to37930
 
1.4%
a27739
 
1.0%
on26369
 
1.0%
as26345
 
1.0%
Other values (156353)2271727
82.3%

Most occurring characters

ValueCountFrequency (%)
104470
100.0%

Most occurring categories

ValueCountFrequency (%)
Control104470
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
104470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common104470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
104470
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII104470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
104470
100.0%

user_created
Categorical

HIGH CARDINALITY

Distinct77112
Distinct (%)39.9%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2021-05-17 18:56:45
 
11678
2021-06-29 09:33:13
 
8879
2021-05-26 19:13:22
 
4851
2021-06-21 08:44:34
 
3804
2010-09-20 17:01:08
 
2619
Other values (77107)
161441 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59192 ?
Unique (%)30.6%

Sample

1st row2009-04-08 17:52:46
2nd row2009-09-21 15:27:30
3rd row2020-06-25 23:30:28
4th row2008-09-10 11:28:53
5th row2020-04-23 17:58:42

Common Values

ValueCountFrequency (%)
2021-05-17 18:56:4511678
 
6.0%
2021-06-29 09:33:138879
 
4.6%
2021-05-26 19:13:224851
 
2.5%
2021-06-21 08:44:343804
 
2.0%
2010-09-20 17:01:082619
 
1.4%
2021-05-22 15:14:511851
 
1.0%
2021-05-23 16:18:161719
 
0.9%
2014-05-16 04:25:35688
 
0.4%
2020-05-21 15:54:09641
 
0.3%
2020-08-11 09:12:38443
 
0.2%
Other values (77102)156099
80.8%

Length

2021-10-30T17:05:35.065946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-05-1711752
 
3.0%
18:56:4511679
 
3.0%
2021-06-298910
 
2.3%
09:33:138881
 
2.3%
2021-05-264890
 
1.3%
19:13:224852
 
1.3%
2021-06-213847
 
1.0%
08:44:343806
 
1.0%
2010-09-202663
 
0.7%
17:01:082623
 
0.7%
Other values (55826)322641
83.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

user_followers
Real number (ℝ≥0)

Distinct20166
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92187.41612
Minimum0
Maximum16069626
Zeros1808
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-10-30T17:05:35.227513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q169
median362
Q31551
95-th percentile63059
Maximum16069626
Range16069626
Interquartile range (IQR)1482

Descriptive statistics

Standard deviation807928.9225
Coefficient of variation (CV)8.763982727
Kurtosis195.9178955
Mean92187.41612
Median Absolute Deviation (MAD)345
Skewness13.0618653
Sum1.781724629 × 1010
Variance6.527491438 × 1011
MonotonicityNot monotonic
2021-10-30T17:05:35.402081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
243628
 
1.9%
182152
 
1.1%
232115
 
1.1%
11022033
 
1.1%
222002
 
1.0%
10701850
 
1.0%
01808
 
0.9%
11161722
 
0.9%
10581626
 
0.8%
11589
 
0.8%
Other values (20156)172747
89.4%
ValueCountFrequency (%)
01808
0.9%
11589
0.8%
21167
0.6%
31241
0.6%
4956
0.5%
51138
0.6%
6819
0.4%
7878
0.5%
8842
0.4%
91414
0.7%
ValueCountFrequency (%)
160696262
< 0.1%
160470431
< 0.1%
160235201
< 0.1%
160157901
< 0.1%
160157872
< 0.1%
160128331
< 0.1%
159967531
< 0.1%
159842271
< 0.1%
159807701
< 0.1%
159726841
< 0.1%

user_friends
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct7718
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean991.3018854
Minimum0
Maximum582461
Zeros23193
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-10-30T17:05:35.594569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130
median236
Q3800
95-th percentile4074
Maximum582461
Range582461
Interquartile range (IQR)770

Descriptive statistics

Standard deviation5334.092691
Coefficient of variation (CV)5.380896344
Kurtosis2693.408941
Mean991.3018854
Median Absolute Deviation (MAD)234
Skewness41.73075676
Sum191590898
Variance28452544.83
MonotonicityNot monotonic
2021-10-30T17:05:35.771060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
023193
 
12.0%
212272
 
6.3%
31171
 
0.6%
1861
 
0.4%
236755
 
0.4%
6703
 
0.4%
25620
 
0.3%
10552
 
0.3%
45547
 
0.3%
7532
 
0.3%
Other values (7708)152066
78.7%
ValueCountFrequency (%)
023193
12.0%
1861
 
0.4%
212272
6.3%
31171
 
0.6%
4464
 
0.2%
5428
 
0.2%
6703
 
0.4%
7532
 
0.3%
8499
 
0.3%
9376
 
0.2%
ValueCountFrequency (%)
5824611
< 0.1%
5165781
< 0.1%
3838381
< 0.1%
3804281
< 0.1%
3803622
< 0.1%
3803531
< 0.1%
3802651
< 0.1%
3005501
< 0.1%
2792171
< 0.1%
2782061
< 0.1%

user_favourites
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct35188
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11999.53491
Minimum0
Maximum1221784
Zeros8692
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-10-30T17:05:35.962547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q157
median1005
Q37324
95-th percentile59290.45
Maximum1221784
Range1221784
Interquartile range (IQR)7267

Descriptive statistics

Standard deviation38962.80402
Coefficient of variation (CV)3.247026181
Kurtosis143.6277624
Mean11999.53491
Median Absolute Deviation (MAD)1004
Skewness9.47313346
Sum2319174112
Variance1518100097
MonotonicityNot monotonic
2021-10-30T17:05:36.160020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19866
 
5.1%
08692
 
4.5%
27402
 
3.8%
575022
 
2.6%
584769
 
2.5%
551356
 
0.7%
3867
 
0.4%
4854
 
0.4%
53773
 
0.4%
2182640
 
0.3%
Other values (35178)153031
79.2%
ValueCountFrequency (%)
08692
4.5%
19866
5.1%
27402
3.8%
3867
 
0.4%
4854
 
0.4%
5505
 
0.3%
6434
 
0.2%
7459
 
0.2%
8377
 
0.2%
9320
 
0.2%
ValueCountFrequency (%)
12217841
< 0.1%
12148131
< 0.1%
12137941
< 0.1%
12058781
< 0.1%
11722841
< 0.1%
11664591
< 0.1%
9561841
< 0.1%
9482461
< 0.1%
9479011
< 0.1%
9461182
< 0.1%

user_verified
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size188.9 KiB
False
176937 
True
 
16335
ValueCountFrequency (%)
False176937
91.5%
True16335
 
8.5%
2021-10-30T17:05:36.298648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct187514
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2021-06-30 04:30:00
 
6
2021-06-18 13:13:23
 
6
2021-06-18 16:50:32
 
5
2021-06-24 09:41:12
 
4
2021-06-24 18:30:32
 
4
Other values (187509)
193247 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique182187 ?
Unique (%)94.3%

Sample

1st row2020-12-20 06:06:44
2nd row2020-12-13 16:27:13
3rd row2020-12-12 20:33:45
4th row2020-12-12 20:23:59
5th row2020-12-12 20:17:19

Common Values

ValueCountFrequency (%)
2021-06-30 04:30:006
 
< 0.1%
2021-06-18 13:13:236
 
< 0.1%
2021-06-18 16:50:325
 
< 0.1%
2021-06-24 09:41:124
 
< 0.1%
2021-06-24 18:30:324
 
< 0.1%
2021-07-16 19:07:334
 
< 0.1%
2021-06-24 09:41:194
 
< 0.1%
2021-06-25 00:30:284
 
< 0.1%
2021-07-19 12:18:564
 
< 0.1%
2021-07-19 12:17:014
 
< 0.1%
Other values (187504)193227
> 99.9%

Length

2021-10-30T17:05:36.414340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-03-012911
 
0.8%
2021-06-292626
 
0.7%
2021-07-192482
 
0.6%
2021-04-212423
 
0.6%
2021-06-302414
 
0.6%
2021-07-232372
 
0.6%
2021-06-232225
 
0.6%
2021-06-222153
 
0.6%
2021-04-122049
 
0.5%
2021-06-252024
 
0.5%
Other values (74278)362865
93.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

text
Categorical

HIGH CARDINALITY
UNIFORM

Distinct191553
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Got my second dose of the #Moderna #vaccine today!! 💉💉
 
16
@naashonomics #Covaxin #Ocgn #OCGN #DeltaVariant $ocgn LETS KICK THE DELTA VARIANT'S ASS IN EVERY COUNTRY!!!
 
11
@POTUS Approve #COVAXIN
 
8
BREAKING NEWS : #SputnikV approved for emergency use in India.
 
6
@POTUS I’ll get vaccinated when you allow #Covaxin to be used in the USA. We need alternatives especially those that work.
 
5
Other values (191548)
193226 

Length

Max length162
Median length139
Mean length124.3028737
Min length13

Characters and Unicode

Total characters225746
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique190130 ?
Unique (%)98.4%

Sample

1st rowSame folks said daikon paste could treat a cytokine storm #PfizerBioNTech https://t.co/xeHhIMg1kF
2nd rowWhile the world has been on the wrong side of history this year, hopefully, the biggest vaccination effort we've ev… https://t.co/dlCHrZjkhm
3rd row#coronavirus #SputnikV #AstraZeneca #PfizerBioNTech #Moderna #Covid_19 Russian vaccine is created to last 2-4 years… https://t.co/ieYlCKBr8P
4th rowFacts are immutable, Senator, even when you're not ethically sturdy enough to acknowledge them. (1) You were born i… https://t.co/jqgV18kch4
5th rowExplain to me again why we need a vaccine @BorisJohnson @MattHancock #whereareallthesickpeople #PfizerBioNTech… https://t.co/KxbSRoBEHq

Common Values

ValueCountFrequency (%)
Got my second dose of the #Moderna #vaccine today!! 💉💉16
 
< 0.1%
@naashonomics #Covaxin #Ocgn #OCGN #DeltaVariant $ocgn LETS KICK THE DELTA VARIANT'S ASS IN EVERY COUNTRY!!!11
 
< 0.1%
@POTUS Approve #COVAXIN8
 
< 0.1%
BREAKING NEWS : #SputnikV approved for emergency use in India.6
 
< 0.1%
@POTUS I’ll get vaccinated when you allow #Covaxin to be used in the USA. We need alternatives especially those that work.5
 
< 0.1%
#Moderna #Vaccine in #India soon. #CIPLA gets clearance to import #Vaccine for emergency use authorisation in #India.5
 
< 0.1%
560057, AGE 45+ 0-D1, 44-D2 slots, #COVAXIN on 18-06-2021 @ NEL COVAXIN(Free) #BBMP5
 
< 0.1%
560027, AGE 45+ 99-D1, 100-D2 slots, #COVAXIN on 22-07-2021 @ Agadi HOSPITAL AND RESEARCH(1410Rs) #BBMP5
 
< 0.1%
560076, AGE 45+ 99-D1, 100-D2 slots, #COVAXIN on 27-07-2021 @ Rainbow Royal Meenakshi MallC1(1410Rs) #BBMP5
 
< 0.1%
@Reuters Do you know the meaning of “V” in the name of the first Russian vaccine- #SputnikV? “V” for Victory. Victory over the #pandemic.5
 
< 0.1%
Other values (191543)193201
> 99.9%

Length

2021-10-30T17:05:36.608857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the78794
 
2.4%
covaxin59677
 
1.8%
of54074
 
1.7%
to51383
 
1.6%
50288
 
1.5%
vaccine47493
 
1.5%
and42206
 
1.3%
moderna40014
 
1.2%
in36290
 
1.1%
for34004
 
1.0%
Other values (301848)2753724
84.8%

Most occurring characters

ValueCountFrequency (%)
225746
100.0%

Most occurring categories

ValueCountFrequency (%)
Control225746
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
225746
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common225746
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
225746
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII225746
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
225746
100.0%

hashtags
Categorical

HIGH CARDINALITY
MISSING

Distinct52470
Distinct (%)34.5%
Missing40973
Missing (%)21.2%
Memory size1.5 MiB
['COVAXIN', 'BBMP']
 
11113
['Moderna']
 
8995
['COVAXIN']
 
8746
['Covaxin']
 
7033
['SputnikV']
 
5106
Other values (52465)
111306 

Length

Max length143
Median length22
Mean length28.85611199
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45633 ?
Unique (%)30.0%

Sample

1st row['PfizerBioNTech']
2nd row['coronavirus', 'SputnikV', 'AstraZeneca', 'PfizerBioNTech', 'Moderna', 'Covid_19']
3rd row['whereareallthesickpeople', 'PfizerBioNTech']
4th row['vaccination']
5th row['BidenHarris', 'Election2020']

Common Values

ValueCountFrequency (%)
['COVAXIN', 'BBMP']11113
 
5.7%
['Moderna']8995
 
4.7%
['COVAXIN']8746
 
4.5%
['Covaxin']7033
 
3.6%
['SputnikV']5106
 
2.6%
['Sinopharm']2589
 
1.3%
['Sinovac']2275
 
1.2%
['moderna']2108
 
1.1%
['BBMP', 'Bengaluru', 'CovidVaccine', 'COVISHIELD']1669
 
0.9%
['COVID19']1562
 
0.8%
Other values (52460)101103
52.3%
(Missing)40973
21.2%

Length

2021-10-30T17:05:36.818295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
covaxin48314
 
13.7%
moderna36960
 
10.4%
covid1916747
 
4.7%
sputnikv13543
 
3.8%
bbmp12930
 
3.7%
vaccine11821
 
3.3%
pfizer10199
 
2.9%
sinovac9591
 
2.7%
covidvaccine9322
 
2.6%
covishield8213
 
2.3%
Other values (23656)176181
49.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

source
Categorical

HIGH CARDINALITY

Distinct334
Distinct (%)0.2%
Missing119
Missing (%)0.1%
Memory size1.5 MiB
Twitter for Android
50087 
Twitter Web App
48214 
Twitter for iPhone
41912 
cowin_vaccine_app
11670 
Cowin Vaccination Availability
8525 
Other values (329)
32745 

Length

Max length32
Median length18
Mean length16.91357111
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)< 0.1%

Sample

1st rowTwitter for Android
2nd rowTwitter Web App
3rd rowTwitter for Android
4th rowTwitter Web App
5th rowTwitter for iPhone

Common Values

ValueCountFrequency (%)
Twitter for Android50087
25.9%
Twitter Web App48214
24.9%
Twitter for iPhone41912
21.7%
cowin_vaccine_app11670
 
6.0%
Cowin Vaccination Availability8525
 
4.4%
CowinAlertsBot7945
 
4.1%
TweetDeck6231
 
3.2%
VaxBlr3804
 
2.0%
Twitter for iPad2592
 
1.3%
Instagram2091
 
1.1%
Other values (324)10082
 
5.2%

Length

2021-10-30T17:05:37.001798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
twitter143763
28.5%
for95031
18.9%
android50250
 
10.0%
app48810
 
9.7%
web48241
 
9.6%
iphone41916
 
8.3%
cowin_vaccine_app11670
 
2.3%
cowin8534
 
1.7%
vaccination8525
 
1.7%
availability8525
 
1.7%
Other values (429)38513
 
7.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

retweets
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct412
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.419600356
Minimum0
Maximum11288
Zeros144849
Zeros (%)74.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-10-30T17:05:37.164365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6
Maximum11288
Range11288
Interquartile range (IQR)1

Descriptive statistics

Standard deviation45.74607252
Coefficient of variation (CV)18.906458
Kurtosis26201.73493
Mean2.419600356
Median Absolute Deviation (MAD)0
Skewness133.5361123
Sum467641
Variance2092.703151
MonotonicityNot monotonic
2021-10-30T17:05:37.339864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0144849
74.9%
122348
 
11.6%
27839
 
4.1%
34206
 
2.2%
42602
 
1.3%
51683
 
0.9%
61275
 
0.7%
7954
 
0.5%
8827
 
0.4%
9615
 
0.3%
Other values (402)6074
 
3.1%
ValueCountFrequency (%)
0144849
74.9%
122348
 
11.6%
27839
 
4.1%
34206
 
2.2%
42602
 
1.3%
51683
 
0.9%
61275
 
0.7%
7954
 
0.5%
8827
 
0.4%
9615
 
0.3%
ValueCountFrequency (%)
112881
< 0.1%
76951
< 0.1%
60181
< 0.1%
48511
< 0.1%
41051
< 0.1%
25501
< 0.1%
23601
< 0.1%
22991
< 0.1%
22941
< 0.1%
22471
< 0.1%

favorites
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct914
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.45960615
Minimum0
Maximum25724
Zeros101340
Zeros (%)52.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-10-30T17:05:37.519383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile23
Maximum25724
Range25724
Interquartile range (IQR)2

Descriptive statistics

Standard deviation162.2354804
Coefficient of variation (CV)15.51066819
Kurtosis8698.016319
Mean10.45960615
Median Absolute Deviation (MAD)0
Skewness77.54535765
Sum2021549
Variance26320.3511
MonotonicityNot monotonic
2021-10-30T17:05:37.697938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0101340
52.4%
132825
 
17.0%
214103
 
7.3%
38155
 
4.2%
45325
 
2.8%
53794
 
2.0%
62937
 
1.5%
72301
 
1.2%
81911
 
1.0%
91490
 
0.8%
Other values (904)19091
 
9.9%
ValueCountFrequency (%)
0101340
52.4%
132825
 
17.0%
214103
 
7.3%
38155
 
4.2%
45325
 
2.8%
53794
 
2.0%
62937
 
1.5%
72301
 
1.2%
81911
 
1.0%
91490
 
0.8%
ValueCountFrequency (%)
257241
< 0.1%
228151
< 0.1%
196221
< 0.1%
159441
< 0.1%
151481
< 0.1%
144121
< 0.1%
135701
< 0.1%
123861
< 0.1%
119951
< 0.1%
101751
< 0.1%

is_retweet
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size188.9 KiB
False
193272 
ValueCountFrequency (%)
False193272
100.0%
2021-10-30T17:05:37.826577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Interactions

2021-10-30T17:05:29.342247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:15.775378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:18.262691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:21.339498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:24.108093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:27.755519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:29.649427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:16.187272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:18.590814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:22.468444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:24.533922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:28.005822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:29.943640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:16.432584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:18.823192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:22.730743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:25.417587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:28.298044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:30.256838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:16.648007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:19.220130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:23.087817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:26.422870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:28.557348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:30.560027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:17.226463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:19.515342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:23.402947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:27.216932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:28.822671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:30.845261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:17.807906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:20.256360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:23.837785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:27.485214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:05:29.058008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-10-30T17:05:37.902391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-30T17:05:38.162664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-30T17:05:38.419975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-30T17:05:38.659336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-30T17:05:38.811929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-30T17:05:31.304002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-30T17:05:32.094887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-10-30T17:05:32.932683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-10-30T17:05:33.265759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

iduser_nameuser_locationuser_descriptionuser_createduser_followersuser_friendsuser_favouritesuser_verifieddatetexthashtagssourceretweetsfavoritesis_retweet
01340539111971516416Rachel RohLa Crescenta-Montrose, CAAggregator of Asian American news; scanning diverse sources 24/7/365. RT's, Follows and 'Likes' will fuel me 👩‍💻2009-04-08 17:52:4640516923247False2020-12-20 06:06:44Same folks said daikon paste could treat a cytokine storm #PfizerBioNTech https://t.co/xeHhIMg1kF['PfizerBioNTech']Twitter for Android00False
11338158543359250433Albert FongSan Francisco, CAMarketing dude, tech geek, heavy metal & '80s music junkie. Fascinated by meteorology and all things in the cloud. Opinions are my own.2009-09-21 15:27:30834666178False2020-12-13 16:27:13While the world has been on the wrong side of history this year, hopefully, the biggest vaccination effort we've ev… https://t.co/dlCHrZjkhmNaNTwitter Web App11False
21337858199140118533eli🇱🇹🇪🇺👌Your Bedheil, hydra 🖐☺2020-06-25 23:30:281088155False2020-12-12 20:33:45#coronavirus #SputnikV #AstraZeneca #PfizerBioNTech #Moderna #Covid_19 Russian vaccine is created to last 2-4 years… https://t.co/ieYlCKBr8P['coronavirus', 'SputnikV', 'AstraZeneca', 'PfizerBioNTech', 'Moderna', 'Covid_19']Twitter for Android00False
31337855739918835717Charles AdlerVancouver, BC - CanadaHosting "CharlesAdlerTonight" Global News Radio Network. Weeknights 7 Pacific-10 Eastern - Email comments/ideas to charles@charlesadlertonight.ca2008-09-10 11:28:5349165393321853True2020-12-12 20:23:59Facts are immutable, Senator, even when you're not ethically sturdy enough to acknowledge them. (1) You were born i… https://t.co/jqgV18kch4NaNTwitter Web App4462129False
41337854064604966912Citizen News ChannelNaNCitizen News Channel bringing you an alternative news source from citizen journalists that haven't sold out. Real news & real views2020-04-23 17:58:421525801473False2020-12-12 20:17:19Explain to me again why we need a vaccine @BorisJohnson @MattHancock #whereareallthesickpeople #PfizerBioNTech… https://t.co/KxbSRoBEHq['whereareallthesickpeople', 'PfizerBioNTech']Twitter for iPhone00False
51337852648389832708DeeBirmingham, EnglandGastroenterology trainee, Clinical Research Fellow in IBD, mother to human and fur baby, Canadian in Britain2020-01-26 21:43:12105108106False2020-12-12 20:11:42Does anyone have any useful advice/guidance for whether the COVID vaccine is safe whilst breastfeeding?… https://t.co/EifsyQoeKNNaNTwitter for iPhone00False
61337851215875608579Gunther FehlingerAustria, Ukraine and KosovoEnd North Stream 2 now - the pipeline of corruption, funding Russias war against Ukraine,Georgia, Syria and political intervention in USA and EU must be stopped2013-06-10 17:49:222731500169344False2020-12-12 20:06:00it is a bit sad to claim the fame for success of #vaccination on patriotic competition between USA, Canada, UK and… https://t.co/IfMrAyGyTP['vaccination']Twitter Web App04False
71337850832256176136Dr.Krutika KuppalliNaNID, Global Health, VHF, Pandemic Prep, Emerging Infections, & Health Policy MD| U.S. Congress COVID-19 expert witness x 2 | ELBI 2020 @JHSPH_CHS2019-03-25 04:14:29219245937815True2020-12-12 20:04:29There have not been many bright days in 2020 but here are some of the best \n1. #BidenHarris winning #Election2020… https://t.co/77u4f8XXfx['BidenHarris', 'Election2020']Twitter for iPhone222False
81337850023531347969Erin DespasNaNDesigning&selling on Teespring. Like 90s Disney tv movies, old school WWE. Dislikes Intolerance, hate, bigots and snakes https://t.co/fa5n4gEHgR2009-10-30 17:53:5488715159639False2020-12-12 20:01:16Covid vaccine; You getting it?\n\n #CovidVaccine #covid19 #PfizerBioNTech #Moderna['CovidVaccine', 'covid19', 'PfizerBioNTech', 'Moderna']Twitter Web App21False
91337842295857623042Ch.Amjad AliIslamabad#ProudPakistani #LovePakArmy #PMIK @insafianspower1\n#PoliticalScience #InternationalAffairs \n#PAKUSTV #Newyork #Islamabad2012-11-12 04:18:12671236820469False2020-12-12 19:30:33#CovidVaccine \n\nStates will start getting #COVID19Vaccine Monday, #US says \n#pakustv #NYC #Healthcare #GlobalGoals… https://t.co/MksOvBvs5w['CovidVaccine', 'COVID19Vaccine', 'US', 'pakustv', 'NYC', 'Healthcare', 'GlobalGoals']Twitter Web App00False

Last rows

iduser_nameuser_locationuser_descriptionuser_createduser_followersuser_friendsuser_favouritesuser_verifieddatetexthashtagssourceretweetsfavoritesis_retweet
1932621437378129325002757VaxBLRBengaluru, IndiaHourly updates on FREE and PAID 18+ and 45+ vaccine slot availability across #Bengaluru BBMP,URBAN & RURAL2021-06-21 08:44:342500False2021-09-13 11:30:2518-44 #BBMP #Bengaluru #CovidVaccine Availability for 13/09 at 05:00PM\nFREE Slots \n#COVISHIELD - Dose1:955, Dose2:6… https://t.co/8oWeTDgipp['BBMP', 'Bengaluru', 'CovidVaccine', 'COVISHIELD']VaxBlr01False
1932631437378083510571013VaxBLRBengaluru, IndiaHourly updates on FREE and PAID 18+ and 45+ vaccine slot availability across #Bengaluru BBMP,URBAN & RURAL2021-06-21 08:44:342500False2021-09-13 11:30:1418-44 #URBAN #Bengaluru #CovidVaccine Availability for 13/09 at 05:00PM\nFREE Slots 0\nPAID Slots \n#COVISHIELD - Dose… https://t.co/QC3r2SfxUY['URBAN', 'Bengaluru', 'CovidVaccine', 'COVISHIELD']VaxBlr00False
1932641437374384214183941Goel Medicos8A/460, Durgapuri Extension, WBasant Goel is the owner of Goel Medicos. He comes with experience of more than 20+ years and under his leadership has taken the pharmacy to new heights of succ2021-08-06 10:45:25220False2021-09-13 11:15:32Sputnik Lite Trial Set To Begin in City Hospital \n\n#sputniknews #medical #pharmacy #covid19vaccine #medicine… https://t.co/aQKJQ5WbEZ['sputniknews', 'medical', 'pharmacy', 'covid19vaccine', 'medicine']Twitter Web App00False
1932651437370550309904392VaxBLRBengaluru, IndiaHourly updates on FREE and PAID 18+ and 45+ vaccine slot availability across #Bengaluru BBMP,URBAN & RURAL2021-06-21 08:44:342500False2021-09-13 11:00:1845+ #URBAN #Bengaluru #CovidVaccine Availability for 13/09 at 04:30PM\nFREE Slots 0\nPAID Slots \n#COVISHIELD - Dose1:… https://t.co/XRxnxa3d8w['URBAN', 'Bengaluru', 'CovidVaccine', 'COVISHIELD']VaxBlr00False
1932661437370445653848065Alexander AlimovGeneva, SwitzerlandДипломат средних талантов. Сейчас - зам. Постпреда России при ООН в Женеве/A modestly talented diplomat. Now - Russian Deputy Perm Rep to UN Office in Geneva2019-04-23 16:54:17161039861970True2021-09-13 10:59:53🇸🇲 FM Luca Beccari: ‘We settled on #SputnikV due to #EMA-approved vaccines supply delays for &gt;2 months despite pre-… https://t.co/GHzGucDq1H['SputnikV', 'EMA']Twitter for iPhone13False
1932671437363048310722566VaxBLRBengaluru, IndiaHourly updates on FREE and PAID 18+ and 45+ vaccine slot availability across #Bengaluru BBMP,URBAN & RURAL2021-06-21 08:44:342500False2021-09-13 10:30:3018-44 #BBMP #Bengaluru #CovidVaccine Availability for 13/09 at 04:00PM\nFREE Slots \n#COVISHIELD - Dose1:957, Dose2:1… https://t.co/dEUK2Sfgbk['BBMP', 'Bengaluru', 'CovidVaccine', 'COVISHIELD']VaxBlr01False
1932681437363004245348352VaxBLRBengaluru, IndiaHourly updates on FREE and PAID 18+ and 45+ vaccine slot availability across #Bengaluru BBMP,URBAN & RURAL2021-06-21 08:44:342500False2021-09-13 10:30:1918-44 #URBAN #Bengaluru #CovidVaccine Availability for 13/09 at 04:00PM\nFREE Slots 0\nPAID Slots \n#COVISHIELD - Dose… https://t.co/gKjBSGLmJA['URBAN', 'Bengaluru', 'CovidVaccine', 'COVISHIELD']VaxBlr00False
1932691437362257604849664India PostFremont, CA, USARefresh yourself every morning with IndiaPost news coverage Use #IndiaPostNewsPaper to get featured\nContribute your articles here: https://t.co/hf43HgvGHr…2009-03-24 20:03:3167072317False2021-09-13 10:27:21US Special Envoy for Climate ... - https://t.co/GhqageRfqn \nGet your news featured use #IndiaPostUSA… https://t.co/8iQUpPAqI8['IndiaPostUSA']IndiaPost01False
1932701437361478026268680India PostFremont, CA, USARefresh yourself every morning with IndiaPost news coverage Use #IndiaPostNewsPaper to get featured\nContribute your articles here: https://t.co/hf43HgvGHr…2009-03-24 20:03:3167072317False2021-09-13 10:24:15India's cumulative COVID-19 vaccination coverage surpasses 74.38 cr - https://t.co/LqQAmaKTJp \nGet your news featur… https://t.co/crdty6qW5yNaNIndiaPost00False
1932711437355448588398592VaxBLRBengaluru, IndiaHourly updates on FREE and PAID 18+ and 45+ vaccine slot availability across #Bengaluru BBMP,URBAN & RURAL2021-06-21 08:44:342500False2021-09-13 10:00:1845+ #URBAN #Bengaluru #CovidVaccine Availability for 13/09 at 03:30PM\nFREE Slots 0\nPAID Slots \n#COVISHIELD - Dose1:… https://t.co/OMVx8SNDXv['URBAN', 'Bengaluru', 'CovidVaccine', 'COVISHIELD']VaxBlr00False